GEO series

A Histone-Centric Multi-Omics Study Shows that Increased H3K4 Methylation Sustains Triple Negative Breast Cancer Phenotypes

GSE283103 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 14 samples Submitted 2025/07/31 Platform GPL24676

At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 14 samples

What it is: ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism: Homo sapiens (human)
Topic: Breast cancerThis series is part of our curated briefing on it.
Samples: 14 samples: 14 samples, each with a distinct title — no replicated groupsGroups are read from the sample names, not from the paper. Check the list below.
Data you can get: A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper: A histone-centric multi-omics study shows that increased H3K4 methylation sustains triple-negative breast cancer phenotypesNature communications · 2025

Summary (written by the submitters)

Altered histone post-translational modifications are frequently associated with cancer. Here, we applied mass-spectrometry to study the epigenetic landscapes of breast cancer subtypes, with a particular focus on triple-negative breast cancers (TNBCs), a heterogeneous group lacking welldefined molecular targets and effective therapies.

Read the full summary ↓

The analysis of over 150 tumors revealed epigenetic signatures that discriminate TNBCs from the other BC subtypes, and that distinguish TNBC patients with different prognoses. Employing a multi-OMICs approach integrating epigenomics, transcriptomics, and proteomics data, we investigated the mechanistic role of increased H3K4 methylation in TNBCs, demonstrating that H3K4me2 sustains the expression of genes associated with the TNBC phenotype. Through CRISPR-mediated editing, we established a causal relationship between H3K4me2 and gene expression for several targets. Furthermore, treatment with H3K4 methyltransferase inhibitors reduced TNBC cell growth in vitro and in vivo. Collectively, our result unraveled a novel epigenetic pathway implicated in TNBC pathogenesis and suggests new opportunities for targeted therapy.

Published in

A histone-centric multi-omics study shows that increased H3K4 methylation sustains triple-negative breast cancer phenotypes

Noberini R, Robusti G, Vai A et al. · Nature communications 2025 · PMID 41028718 · doi:10.1038/s41467-025-63745-z

This dataset

Open GSE283103 in finder → Find more human ChIP / ATAC / CUT&Tag datasets → Breast cancer landscape →

Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE283103_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓

3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1191868 and SRA study SRP548236. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41028718) ↗

Study design

14 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 14 sample titles: 14 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study

GSM8656053 MDA-MB-231, DMSO, biol rep1 (INPUT)
GSM8656054 MDA-MB-231, DMSO, biol rep2 (INPUT)
GSM8656055 MDA-MB-231, DMSO, biol rep3 (INPUT)
GSM8656056 MDA-MB-231, DMSO, biol rep1, H3K4me2 (ChIP)
GSM8656057 MDA-MB-231, DMSO, biol rep2, H3K4me2 (ChIP)
GSM8656058 MDA-MB-231, DMSO, biol rep3, H3K4me2 (ChIP)
GSM8656059 MDA-MB-231, OICR-9429, biol rep1 (INPUT)
GSM8656060 MDA-MB-231, OICR-9429, biol rep2 (INPUT)
GSM8656061 MDA-MB-231, OICR-9429, biol rep3 (INPUT)
GSM8656062 MDA-MB-231, OICR-9429, biol rep1, H3K4me2 (ChIP)
GSM8656063 MDA-MB-231, OICR-9429, biol rep2, H3K4me2 (ChIP)
GSM8656064 MDA-MB-231, OICR-9429, biol rep3, H3K4me2 (ChIP)
GSM8656065 MDA-MB-231, Untreated (INPUT)
GSM8656066 MDA-MB-231, Untreated, H3K4me2 (ChIP)

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Search all human ChIP / ATAC / CUT&Tag datasets in GEO →

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.